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Showing papers from University of Toronto & Vector Institute Show all papers

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NeurIPS 2026U TorontoChalmers University of TechnologPrincetonU Toronto & Vector InstituteDistribution shift & OOD detection

When to Trust a PFN: Detecting Harmful Shift in Tabular Foundation Models

Viet Nguyen, Herman Bergström, Stephan Rabanser, Rahul Krishnan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U TorontoBroad InstituteU Toronto & Vector InstituteU Toronto/Vector Institute/SickKRepresentation & contrastive learning

DynaSub: Adaptive Subgrouping for Scalable Representation Learning

Tina Behrouzi, Sana Tonekaboni, Rahul Krishnan, Anna Goldenberg

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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57%Worth a look
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NeurIPS 2026U Toronto & Vector InstituteMila, University of MontrealU TorontoGoogle DeepmindValence LabsMolecules & drug discovery

On the Selectivity of Generative Models in Structure-Based Drug Design

Ella Miray Rajaonson, Jungyoon Lee, William Chau, Alan Aspuru-Guzik and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, BerkeleyEcole Normale Supérieure Paris-SU Toronto & Vector InstituteU California, San DiegoOptimization

Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics

Binghua Li, Mengzhe Li, Denny Wu, Tianhao Wang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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67%Highly rated
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NeurIPS 2026U OxfordU Toronto & Vector InstituteAI oversight & deception

Tight PAC-Bayes Generalisation Guarantees for Large Language Model Safety Monitoring

Tom Lamb, Philip Torr, Tim G. J. Rudner

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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76%Highly rated
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NeurIPS 2026UC BerkeleyPrincetonU Toronto & Vector InstituteFlatiron Institute, Simons FoundOptimization

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory

Spectral optimizer Muon exceeds SGD associative memory capacity, matching Newton's method with first-order updates and larger critical batch sizes.

Juno Kim, Eshaan Nichani, Denny Wu, Alberto Bietti and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 3/5
71%Highly rated
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NeurIPS 2026U TorontoU Toronto & Vector InstituteU British ColumbiaLearning theory

SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws

Multiclass logistic regression learns Gaussian mixtures sequentially by class frequency, producing power-law risk phases and compute-optimal scaling laws.

Konstantinos Tsiolis, Denny Wu, Christos Thrampoulidis, Murat Erdogdu

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 1/5